Data transmission method and apparatus

ABSTRACT

A data transmission method and apparatus are described. The method includes a receive end receiving a first parameter, where the first parameter includes a parameter of a probability distribution of a hidden variable. The hidden variable is obtained by encoding first data by using an encoder of a variational auto-encoder. The receive end determines the probability distribution based on the first parameter, and samples the probability distribution M times to obtain M pieces of sampled data, where M is a positive integer. The receive end reconstructs the first data based on the M pieces of sampled data. According to the described data transmission method and apparatus, the first parameter is transmitted, so that the receive end can support a plurality of times of sampling, thereby reducing an amount of data transmitted over an air interface, and reducing data transmission overhead.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2021/126973, filed on Oct. 28, 2021, which claims priority toChinese Pat. Application No. 202011169163.4, filed on Oct. 28, 2020. Thedisclosures of the aforementioned applications are hereby incorporatedby reference in their entireties.

TECHNICAL FIELD

This application relates to the communication field, and morespecifically, to a data transmission method and apparatus.

BACKGROUND

With continuous evolution of a form and an application scenario of awireless network, a requirement for data transmission in the networkincreases sharply, and data becomes more diversified. For example, in amassive machine-type communication (mMTC) scenario, a large quantity ofdiversified devices access the network, and a data transmission amountincreases sharply. For another example, in a millimeter-waveultra-large-scale antenna system, accurate channel state information(CSI) is required for precoding at a transmit end. However, when aquantity of antennas is large, a CSI feedback data amount is huge. Inthis case, an efficient and more flexible data compression andtransmission solution is required.

A variational auto-encoder (VAE) may be used for data compression andfeature extraction, and has been used in wireless communicationscenarios such as joint source and channel coding and CSI compressionfeedback. However, in an actual application of a joint source andchannel coding scheme, only one-time sampling is performed ondistribution of hidden variables at the transmit end, the sampled datais transmitted to a receive end through a channel, and the receive endobtains reconstructed data by using a decoder based on a result of theone-time sampling. In an existing method, a probability model cannot befully used for the one-time sampling of distribution. To improveprecision, sampling needs to be performed at the transmit end for aplurality of times, and sampled data of each time needs to be separatelytransmitted to the receive end. A plurality of times of datatransmission causes a large data transmission amount and high overheads.

An artificial intelligence (AI) technology has achieved good results inmany fields such as computer vision. A large amount of available data,more computing power, greater flexibility of neural networks, and loweronline reasoning complexity provide a possibility for application of theartificial intelligence technology in wireless communication systems. Alarge amount of data is required for neural network training. A trainingproblem of a neural network module needs to be considered when theneural network module is deployed in a network, and transmission of thetraining data requires a large quantity of overheads.

Therefore, transmitting a large amount of data in a communicationnetwork occupies a large quantity of transmission resources, which is anurgent problem to be resolved.

SUMMARY

This application provides a data transmission method and apparatus. Whena large amount of data needs to be transmitted, a receive end cansupport a plurality of times of sampling by using a parameter fortransmitting data, so that the large amount of data does not need to betransmitted, and data transmission overheads can be reduced.

According to a first aspect, a data transmission method is provided. Themethod includes: A receive end receives a first parameter, where thefirst parameter includes a parameter of a probability distribution of ahidden variable, and the hidden variable is obtained by encoding firstdata by using an encoder of a variational auto-encoder; the receive enddetermines the probability distribution based on the first parameter;the receive end samples the probability distribution for M times, toobtain M pieces of sampled data, where M is a positive integer; and thereceive end reconstructs the first data based on the M pieces of sampleddata.

Therefore, in this embodiment of this application, a first parameter istransmitted, so that a receive end can generate a probabilitydistribution of a hidden variable based on the first parameter, tosample the probability distribution for a plurality of times, therebyreducing an amount of data transmitted over an air interface andreducing overheads.

With reference to the first aspect, in some implementations of the firstaspect, that the receive end reconstructs the first data based on the Mpieces of sampled data includes: The receive end separately decodes theM pieces of sampled data by using a decoder of the variationalauto-encoder, to obtain M pieces of second data; and the receive enddetermines reconstructed first data based on the M pieces of seconddata. That the receive end determines reconstructed first data based onthe M pieces of second data includes: The receive end averages the Mpieces of second data, and uses an averaging result as the reconstructedfirst data; the receive end performs selection on the M pieces of seconddata, and uses a selection result as the reconstructed first data; orthe receive end averages the M pieces of second data to obtain thirddata, and the receive end performs a decision on the third data, anduses a decision result as the reconstructed first data.

In this case, the receive end may separately decode data sampled for aplurality of times, and process the decoded data, to obtainreconstructed first data, so that the receive end can support aplurality of times of sampling in a case of one transmission, anddetermine the reconstructed first data based on a result of theplurality of times of sampling.

With reference to the first aspect, in some implementations of the firstaspect, before the receive end receives the first parameter, the methodfurther includes: The receive end sends first indication information,where the first indication information indicates a transmit end to sendthe first parameter.

In this case, the receive end may determine, based on a factor such as aprocessing capability of the receive end or a precision requirement forreconstructing data, that data that can be received is the firstparameter, and send the first indication information to the transmitend, to indicate the transmit end to send the first parameter.

With reference to the first aspect, in some implementations of the firstaspect, the method further includes: The receive end receives secondindication information, where the second indication informationindicates the receive end to sample the probability distribution. Thatthe receive end samples the probability distribution for M timesincludes: The receive end samples the probability distribution for the Mtimes based on the second indication information.

In this case, if the transmit end determines to send the firstparameter, the transmit end may send the second indication informationto the receive end, to indicate the receive end to sample theprobability distribution. After receiving the second indicationinformation, the receive end may sample the probability distribution forthe M times based on the second indication information.

According to a second aspect, a data transmission method is provided.The method includes: A transmit end determines first data; the transmitend determines a first parameter based on the first data, where thefirst parameter includes a parameter of a probability distribution of ahidden variable, and the hidden variable is obtained by encoding thefirst data by using an encoder of a variational auto-encoder; and thetransmit end sends the first parameter.

Therefore, in this embodiment of this application, a first parameter istransmitted, so that a receive end can support a plurality of times ofsampling, and determine reconstructed first data based on a result ofthe plurality of times of sampling, thereby reducing an amount of datatransmitted over an air interface, and reducing overheads.

With reference to the second aspect, in some implementations of thesecond aspect, the method further includes: The transmit end receivesfirst indication information, where the first indication informationindicates the transmit end to send the first parameter. That thetransmit end sends the first parameter includes: The transmit end sendsthe first parameter based on the first indication information.

With reference to the second aspect, in some implementations of thesecond aspect, the method further includes: The transmit end sendssecond indication information, where the second indication informationindicates a receive end to sample the probability distribution.

According to a third aspect, a data transmission method is provided. Themethod includes: A receive end receives a first parameter, where thefirst parameter includes a characteristic parameter of a function, thefunction is obtained by modeling data in a first dataset, and the firstdataset includes at least one piece of to-be-sent data; the receive enddetermines the function based on the first parameter; and the receiveend samples the function for M times, to reconstruct the data in thefirst dataset, where M is a positive integer.

Therefore, in this embodiment of this application, a first parameter istransmitted, so that a receive end can determine a function based on thefirst parameter, sample the function for a plurality of times, and usedata sampled for the plurality of times as reconstructed data in a firstdataset. Therefore, original data does not need to be transmitted, andoverheads can be reduced.

With reference to the third aspect, in some implementations of the thirdaspect, the function includes a Gaussian mixture model, and the firstparameter includes at least one of a mean vector, a covariance matrix,or a mixing coefficient of the Gaussian mixture model.

With reference to the third aspect, in some implementations of the thirdaspect, the function includes a generative adversarial network GAN, andthe first parameter includes at least one of a weight or a bias of theGAN.

With reference to the third aspect, in some implementations of the thirdaspect, the first parameter further includes a quantity N of pieces ofdata in the first dataset, and N is a positive integer.

In this case, a transmit end may send a quantity of pieces of originaldata, and a quantity of pieces of data generated by a receive end is amultiple of the quantity of pieces of original data. In this way, thedata can be better used for training.

With reference to the third aspect, in some implementations of the thirdaspect, the method further includes: The receive end sends firstindication information, where the first indication information indicatesa transmit end to send the first parameter.

In this case, the receive end may determine, based on a factor such as aprocessing capability of the receive end or a precision requirement fordata transmission, that data that can be received is the firstparameter, and send the first indication information to the transmitend, to indicate the transmit end to send the first parameter.

With reference to the third aspect, in some implementations of the thirdaspect, the method further includes: The receive end receives secondindication information, where the second indication informationindicates the receive end to sample a probability distribution. That thereceive end samples the probability distribution for M times includes:The receive end samples the probability distribution for the M timesbased on the second indication information.

In this case, if the transmit end determines to send the firstparameter, the transmit end may send the second indication informationto the receive end, to indicate the receive end to sample theprobability distribution. After receiving the second indicationinformation, the receive end may sample the probability distribution forthe M times based on the second indication information.

According to a fourth aspect, a data transmission method is provided.The method includes: A transmit end determines a first dataset, wherethe first dataset includes at least one piece of to-be-sent data; thetransmit end determines a first parameter based on the first dataset,where the first parameter includes a characteristic parameter of afunction, and the function is obtained by modeling the data in the firstdataset; and the transmit end sends the first parameter.

Therefore, in this embodiment of this application, a first parameter istransmitted, so that a receive end can support a plurality of times ofsampling, and use data sampled for the plurality of times asreconstructed data in a first dataset. Therefore, original data does notneed to be transmitted, and overheads can be reduced.

With reference to the fourth aspect, in some implementations of thefourth aspect, the function includes a Gaussian mixture model, and thefirst parameter includes at least one of a mean vector, a covariancematrix, or a mixing coefficient of the Gaussian mixture model.

With reference to the fourth aspect, in some implementations of thefourth aspect, the function includes a generative adversarial networkGAN, and the first parameter includes at least one of a weight or a biasof the GAN.

With reference to the fourth aspect, in some implementations of thefourth aspect, the first parameter further includes a quantity N ofpieces of data in the first dataset, and N is a positive integer.

With reference to the fourth aspect, in some implementations of thefourth aspect, the method further includes: The transmit end receivesfirst indication information, where the first indication informationindicates the transmit end to send the first parameter. That thetransmit end sends the first parameter includes: The transmit end sendsthe first parameter based on the first indication information.

With reference to the fourth aspect, in some implementations of thefourth aspect, the method further includes: The transmit end sendssecond indication information, where the second indication informationindicates a receive end to sample a probability distribution.

With reference to the fourth aspect, in some implementations of thefourth aspect, before the transmit end determines the first dataset, themethod further includes: The transmit end determines a second dataset,where the second dataset includes at least one piece of to-be-sent data;the transmit end clusters the data in the second dataset to obtain Sdata subsets, where S is a positive integer; and the transmit endseparately determines the S data subsets as the first dataset.

In this case, the transmit end may cluster the to-be-sent data in thesecond dataset based on a status of the data in the second dataset, andthen separately determine the clustered data as the first dataset. Inthis way, accuracy of sampling by the receive end can be furtherimproved while transmission resources are saved.

With reference to the fourth aspect, in some implementations of thefourth aspect, the method further includes: The transmit end sends atleast one piece of first data, and correspondingly, the receive endreceives the at least one piece of first data.

In this embodiment of this application, the data received by the receiveend further includes some data in the first dataset. The receive enduses the data and the data obtained by the receive end through samplingas a real sample for neural network training. In this way, authenticityof a training result can be improved.

According to a fifth aspect, a data transmission apparatus is provided.The apparatus includes: a transceiver unit, configured to receive afirst parameter, where the first parameter includes a parameter of aprobability distribution of a hidden variable, and the hidden variableis obtained by encoding first data by using an encoder of a variationalauto-encoder; and a processing unit, configured to: determine theprobability distribution based on the first parameter. The processingunit is further configured to sample the probability distribution for Mtimes, to obtain M pieces of sampled data, where M is a positiveinteger. The processing unit is further configured to reconstruct thefirst data based on the M pieces of sampled data.

With reference to the fifth aspect, in some implementations of the fifthaspect, the processing unit is specifically configured to: separatelydecode the M pieces of sampled data by using a decoder of thevariational auto-encoder, to obtain M pieces of second data; and averagethe M pieces of second data, and use an averaging result asreconstructed first data; perform selection on the M pieces of seconddata, and use a selection result as the reconstructed first data; oraverage the M pieces of second data to obtain third data, perform adecision on the third data, and use a decision result as thereconstructed first data.

With reference to the fifth aspect, in some implementations of the fifthaspect, the transceiver unit is further configured to send firstindication information, where the first indication information indicatesa transmit end to send the first parameter.

With reference to the fifth aspect, in some implementations of the fifthaspect, the transceiver unit is further configured to receive secondindication information, where the second indication informationindicates a receive end to sample the probability distribution.

According to a sixth aspect, a data transmission apparatus is provided.The apparatus includes: a processing unit, configured to determine afirst data, where the processing unit is further configured to determinea first parameter based on the first data, where the first parameterincludes a parameter of a probability distribution of a hidden variable,and the hidden variable is obtained by encoding the first data by usingan encoder of a variational auto-encoder; and a transceiver unit,configured to send the first parameter.

With reference to the sixth aspect, in some implementations of the sixthaspect, the transceiver unit is further configured to receive firstindication information, where the first indication information indicatesa transmit end to send the first parameter.

With reference to the sixth aspect, in some implementations of the sixthaspect, the transceiver unit is further configured to send secondindication information, where the second indication informationindicates a receive end to sample the probability distribution.

According to a seventh aspect, a data transmission apparatus isprovided. The apparatus includes: a transceiver unit, configured toreceive a first parameter, where the first parameter includes acharacteristic parameter of a function, the function is obtained bymodeling data in a first dataset, and the first dataset includes atleast one piece of to-be-sent data; and a processing unit, configured todetermine the function based on the first parameter. The processing unitis further configured to sample the function for M times, to reconstructthe data in the first dataset, where M is a positive integer.

With reference to the seventh aspect, in some implementations of theseventh aspect, the function includes a Gaussian mixture model, and thefirst parameter includes at least one of a mean vector, a covariancematrix, or a mixing coefficient of the Gaussian mixture model.

With reference to the seventh aspect, in some implementations of theseventh aspect, the function includes a generative adversarial networkGAN, and the first parameter includes at least one of a weight or a biasof the GAN.

With reference to the seventh aspect, in some implementations of theseventh aspect, the first parameter further includes a quantity N ofpieces of data in the first dataset, and N is a positive integer.

With reference to the seventh aspect, in some implementations of theseventh aspect, the transceiver unit is further configured to send firstindication information, where the first indication information indicatesa transmit end to send the first parameter.

With reference to the seventh aspect, in some implementations of theseventh aspect, the transceiver unit is further configured to receivesecond indication information, where the second indication informationindicates a receive end to sample a probability distribution.

According to an eighth aspect, a data transmission apparatus isprovided. The apparatus includes: a processing unit, configured todetermine a first dataset, where the first dataset includes at least onepiece of to-be-sent data, and the processing unit is further configuredto determine a first parameter based on the first dataset, where thefirst parameter includes a characteristic parameter of a function, andthe function is obtained by modeling the data in the first dataset; anda transceiver unit, configured to send the first parameter.

With reference to the eighth aspect, in some implementations of theeighth aspect, the function includes a Gaussian mixture model, and thefirst parameter includes at least one of a mean vector, a covariancematrix, or a mixing coefficient of the Gaussian mixture model.

With reference to the eighth aspect, in some implementations of theeighth aspect, the function includes a generative adversarial networkGAN, and the first parameter includes at least one of a weight or a biasof the GAN.

With reference to the eighth aspect, in some implementations of theeighth aspect, the first parameter further includes a quantity N ofpieces of data in the first dataset, and N is a positive integer.

With reference to the eighth aspect, in some implementations of theeighth aspect, the transceiver unit is further configured to send firstindication information, where the first indication information indicatesa transmit end to send the first parameter.

With reference to the eighth aspect, in some implementations of theeighth aspect, the transceiver unit is further configured to receivesecond indication information, where the second indication informationindicates a receive end to sample a probability distribution.

With reference to the eighth aspect, in some implementations of theeighth aspect, the processing unit is further configured to: determine asecond dataset, where the second dataset includes at least one piece ofto-be-sent data; cluster the data in the second dataset to obtain S datasubsets, where S is a positive integer; and separately determine the Sdata subsets as the first dataset.

According to a ninth aspect, a communication system is provided,including a transmit end and a receive end. The receive end isconfigured to perform the method in the implementations of the firstaspect or the third aspect, and the transmit end is configured toperform the method in the implementations of the second aspect or thefourth aspect.

According to a tenth aspect, a computer program product is provided. Thecomputer program product includes a computer program (which may also bereferred to as code or instructions). When the computer program is run,a computer is enabled to perform the method according to any one of thefirst aspect to the fourth aspect and the possible implementations ofthe first aspect to the fourth aspect.

According to an eleventh aspect, a computer-readable medium is provided.The computer-readable medium stores a computer program (which may alsobe referred to as code or instructions). When the computer program isrun on a computer, the computer is enabled to perform the methodaccording to any one of the first aspect to the fourth aspect and thepossible implementations of the first aspect to the fourth aspect.

According to a twelfth aspect, a chip system is provided, including amemory and a processor. The memory is configured to store a computerprogram, and the processor is configured to invoke the computer programfrom the memory and run the computer program, so that a communicationdevice on which the chip system is installed performs the method in anyone of the first aspect to the fourth aspect and the possibleimplementations of the first aspect to the fourth aspect.

The chip system may include an input circuit or interface configured tosend information or data, and an output circuit or interface configuredto receive information or data.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of three application scenarios accordingto an embodiment of this application;

FIG. 2 is a schematic flowchart of a data transmission method accordingto an embodiment of this application;

FIG. 3 is a schematic flowchart of a data transmission method accordingto another embodiment of this application;

FIG. 4 is a schematic block diagram of an example of a data transmissionapparatus according to this application;

FIG. 5 is a schematic block diagram of another example of a datatransmission apparatus according to this application;

FIG. 6 is a schematic block diagram of another example of a datatransmission apparatus according to this application; and

FIG. 7 is a schematic block diagram of another example of a datatransmission apparatus according to this application.

DESCRIPTION OF EMBODIMENTS

The following describes technical solutions of this application withreference to accompanying drawings.

The technical solutions in embodiments of this application may beapplied to various communication systems, for example, a global systemfor mobile communication (GSM), a code division multiple access (CDMA)system, a wideband code division multiple access (WCDMA) system, ageneral packet radio service (GPRS) system, a Long Term Evolution (LTE)system, an LTE frequency division duplex (FDD) system, an LTE timedivision duplex (TDD) system, a universal mobile telecommunicationsystem (UMTS), a Worldwide Interoperability For Microwave Access (WiMAX)communication system, a narrowband Internet of Things (NB-IoT) system, a5th generation (5G) system or new radio (NR) system, three applicationscenarios of a 5G mobile communication system (namely, enhanced mobilebroadband, eMBB), ultra-reliable and ultra-low latency communication(URLLC), and enhanced machine type communication (eMTC)), and acommunication system that may appear in the future.

A data transmission method in this application may be used for datatransmission between a transmit end and a receive end. The transmit endin embodiments of this application may be user equipment, an accessterminal, a subscriber unit, a subscriber station, a mobile station, amobile console, a remote station, a remote terminal, a mobile device, auser terminal, a terminal, a wireless communication device, a useragent, or a user apparatus. The terminal device may alternatively be acellular phone, a cordless phone, a session initiation protocol (SIP)phone, a wireless local loop (WLL) station, a personal digital assistant(PDA), a handheld device having a wireless communication function, acomputing device, another processing device connected to a wirelessmodem, a vehicle-mounted device, a wearable device, a terminal device ina 5G network, a terminal device in a future evolved public land mobilenetwork (PLMN), or the like. This is not limited in embodiments of thisapplication. The transmit end in embodiments of this application mayalternatively be a network device, and the network device may be a radioaccess network (radio access network, RAN) device. The RAN device mayinclude various types of base stations. For example, the base stationsin embodiments of this application may include macro base stations,micro base stations, relay stations, access points, and the like invarious forms. In systems using different radio access technologies,names of devices that have a base station function may be different. Forexample, in an LTE network, a device with a base station function isreferred to as an evolved NodeB (eNB or eNodeB), and in a 3rd generation(3G) network, a device with a base station function is referred to asaNodeB. In a 5th generation (5G) network, a device with a base stationfunction is referred to as a gNodeB (g NodeB, gNB) or the like. This isnot limited in embodiments of this application.

The receive end in embodiments of this application may be userequipment, an access terminal, a subscriber unit, a subscriber station,a mobile station, a mobile console, a remote station, a remote terminal,a mobile device, a user terminal, a terminal, a wireless communicationdevice, a user agent, or a user apparatus. The terminal device mayalternatively be a cellular phone, a cordless phone, a sessioninitiation protocol (SIP) phone, a wireless local loop (WLL) station, apersonal digital assistant (PDA), a handheld device having a wirelesscommunication function, a computing device, another processing deviceconnected to a wireless modem, a vehicle-mounted device, a wearabledevice, a terminal device in a 5G network, a terminal device in a futureevolved public land mobile network (PLMN), or the like. This is notlimited in embodiments of this application. The receive end inembodiments of this application may alternatively be a network device,and the network device may be a radio access network (RAN) device. TheRAN device may include various types of base stations. For example, thebase stations in embodiments of this application may include macro basestations, micro base stations, relay stations, access points, and thelike in various forms. In systems using different radio accesstechnologies, names of devices that have a base station function may bedifferent. For example, in an LTE network, a device with a base stationfunction is referred to as an evolved NodeB (eNB or eNodeB), and in a3rd generation (3G) network, a device with a base station function isreferred to as a NodeB. In a 5th generation (5G) network, a device witha base station function is referred to as a gNodeB (g NodeB, gNB) or thelike. This is not limited in embodiments of this application.

FIG. 1 is a schematic diagram of using a communication system 100according to this application. As shown in FIG. 1(a), the communicationsystem 100 may include a terminal device 110 and a network device 120.For specific descriptions of the terminal device 110 and the networkdevice 120, refer to the foregoing related descriptions. The terminaldevice may access a network by using the network device, and theterminal device and the network device may communicate with each otherthrough a radio link. As shown in FIG. 1(b), the communication system100 may include the terminal device 110 and a terminal device 130. Theterminal device 110 and the terminal device 130 perform datatransmission through a device-to-device (D2D) link. The D2D link, alsoreferred to as a sidelink (sidelink), is a link over which two devicesdirectly communicate with each other without using a third party. Asshown in FIG. 1(c), the communication system 100 may include the networkdevice 120 and a network device 140. The network device 120 and thenetwork device 140 perform transmission through a device-to-device D2Dlink.

Therefore, the data transmission method in this application may be usedfor communication between a network device and a terminal device, or maybe used for communication between network devices, or may be used forcommunication between terminal devices. The three application scenariosin FIG. 1 are merely used as examples. This is not limited in thisapplication.

FIG. 2 is a schematic flowchart of a data transmission method 200according to an embodiment of this application. Before the method isused, an encoder and a decoder are first jointly trained based on a VAE,in other words, the encoder and the decoder jointly form a neuralnetwork, and are jointly trained. A trained decoder may decode a resultoutput by the encoder, to reconstruct input data of the encoder. Atrained encoder and the trained decoder are respectively built in atransmit end and a receive end. It should be noted that there may be oneor more encoders of the VAE, and similarly, there may be one or moredecoders of the VAE. In other words, the encoder and the decoder areobtained through joint training, but it does not mean that a quantity ofencoders and a quantity of decoders are definitely the same. Thequantity of encoders and the quantity of decoders are not limited inthis embodiment.

A training process of the VAE is to maximize an expectationE(logp_(θ)(x)) of a marginal log-likelihood probability logp_(θ)(x), inother words, to maximize a probability that reconstructed data is equalto sent data.

A lower bound of a variation of logp_(θ)(x) is obtained throughcalculation:

log  p_(θ)(x) ≥ −D_(KL)(q_(ϕ)((z|x))||p_(θ)(z))) + E_(q_(ϕ)(z|x)))[log  p_(θ)(x|z))]

ϕ is a parameter of the encoder of the VAE, and θ is a parameter of thedecoder of the VAE. x represents a sample of input original data,p_(θ)(_(x)) represents a probability of outputting data x by the decoderof the VAE, z represents a hidden variable of the original data, aprobability distribution of the hidden variable is p_(θ)(z), qϕ (z|x)represents an approximate conditional probability distribution of z whenx is given for the encoder of the VAE, p_(θ)(x|z) represents aconditional probability distribution of x when z is known for thedecoder of the VAE, D_(KL) represents a Kullback-Leibler divergence, andE represents an expectation of a corresponding variable. Optimizing theVAE is to maximize E(logp_(θ)(x)) by jointly training the encoder andthe decoder.

The method 200 may be applied to the scenarios shown in FIG. 1 , andcertainly, may also be applied to another communication scenario. Thisis not limited in embodiments of this application.

In S210, the transmit end determines first data, where the first data isto-be-sent data.

In S220, the transmit end determines a first parameter based on thefirst data, where the first parameter includes a parameter of aprobability distribution of a hidden variable, and the hidden variableis obtained by encoding the first data by using the encoder of the VAE.

Specifically, the transmit end inputs the first data into the encoder ofthe VAE, and the encoder encodes the first data to obtain the hiddenvariable of the first data. The hidden variable may be data thatcomplies with a probability distribution. For example, the probabilitydistribution may be a Gaussian distribution, a Bemoulli distribution, orthe like. Further, the encoder calculates the first parameter, where thefirst parameter includes the parameter of the probability distributionof the hidden variable of the first data. For example, if theprobability distribution of the hidden variable is the Gaussiandistribution, the first parameter is a mean value and a variance of theGaussian distribution. For another example, if the probabilitydistribution of the hidden variable is the Bernoulli distribution, thefirst parameter is a probability of the Bernoulli distribution. Adimension of the hidden variable is not greater than a dimension of thefirst data. Further, the encoder outputs the first parameter.

In S230, the transmit end sends the first parameter, andcorrespondingly, the receive end receives the first parameter.Specifically, the transmit end may send the first parameter to thereceive end through an existing channel, or may set a dedicated channelto send the first parameter to the receive end.

As an example instead of a limitation, when UE transmits CSI to a basestation, a first parameter may be transmitted through an existingphysical uplink control channel (PUCCH) or a physical uplink sharedchannel (PUSCH). Alternatively, a dedicated CSI channel may be set fortransmission.

In S240, the receive end generates the probability distribution of thehidden variable based on the first parameter.

Specifically, if the first parameter is the mean value and the varianceof the Gaussian distribution, the receive end may generate the Gaussiandistribution based on the first parameter. If the first parameter is aprobability parameter of the Bernoulli distribution, the receive end maygenerate the Bernoulli distribution based on the probability parameter.

In S250, the receive end samples the probability distribution for Mtimes, to obtain M pieces of sampled data, where M is a positiveinteger.

Specifically, if the probability distribution is the Gaussiandistribution, a normal distribution sampling method may be used. If theprobability distribution is the Bernoulli distribution, evendistribution sampling may be used. A sampling method is not limited inembodiments of this application.

In S260, the receive end reconstructs the first data based on the Mpieces of sampled data.

In a possible implementation, in S260, that the receive end reconstructsthe first data based on the M pieces of sampled data includes: thereceive end separately decodes the M pieces of sampled data by using thedecoder of the VAE, to obtain M pieces of second data; and the receiveend determines reconstructed first data based on the M pieces of seconddata.

Specifically, that the receive end determines reconstructed first databased on the M pieces of second data includes: the receive end mayaverage the M pieces of second data, and use an average result as thereconstructed first data, where specifically, an averaging manner may beany one of the following: calculating an arithmetic average, a weightedaverage, a geometric average, a harmonic average, or a mean squareaverage of the M pieces of first data, and using a calculation result asthe reconstructed first data.

In a possible implementation, the receive end may perform selection onthe M pieces of second data, and use a selection result as thereconstructed first data. Specifically, a selection manner may be:selecting data that appears most frequently in the M pieces of seconddata as the reconstructed first data.

In a possible implementation, the receive end may average the M piecesof second data to obtain third data, and the receive end performs adecision on the third data, and uses a decision result as thereconstructed first data. A specific decision manner may be a harddecision.

Preferably, M is a positive integer greater than 1.

Therefore, in this embodiment of this application, the transmit endtransmits the first parameter, so that the receive end can generate theprobability distribution of the hidden variable based on the firstparameter, to sample the probability distribution for a plurality oftimes, thereby reducing an amount of data transmitted over an airinterface, and reducing overheads.

In a possible implementation, the method further includes: The transmitend sends second indication information, where the second indicationinformation indicates the receive end to sample the probabilitydistribution. Correspondingly, the receive end receives the secondindication information, and samples the probability distribution basedon the second indication information. A quantity of sampling times maybe M.

In this case, if the transmit end determines to send the firstparameter, the transmit end may send the second indication informationto the receive end, to indicate the receive end to sample theprobability distribution. After receiving the second indicationinformation, the receive end may sample the probability distribution forthe M times based on the second indication information.

In a possible implementation, the method further includes: The transmitend first determines a first dataset, where the first dataset includes aplurality of groups of data; and the transmit end separately determineseach group of data in the first dataset as the first data.

In a possible implementation, the method further includes: The receiveend sends first indication information, where the first indicationinformation indicates the transmit end to send the first parameter; andcorrespondingly, the transmit end receives the first indicationinformation, and sends the first parameter based on the first indicationinformation.

In a possible implementation, the first indication information mayfurther indicate the transmit end to send the sampled data of the firstparameter. The receive end may determine, based on a factor such as aprocessing capability of the receive end or a precision requirement forreconstructing data, that data that can be received is the firstparameter or the sampled data of the first parameter, and send the firstindication information to the transmit end. When the first indicationinformation indicates the transmit end to send the sampled data of thefirst parameter, after the receive end receives the first indicationinformation, the receive end and the transmit end may perform signalinginteraction by using a method in the conventional technology.

As an example instead of a limitation, when UE transmits channel stateinformation (CSI) to a base station, the base station may add firstindication information to downlink control information (DCI). A quantityof bits of the first indication information is 1, and the firstindication information indicates that data sent by a transmit end is afirst parameter or sampled data of the first parameter. As shown inTable 1, when a bit value is 1, the indication information indicatesthat the CSI transmitted by a user to the base station is the firstparameter; or when a bit value is 0, the indication informationindicates that the CSI transmitted by a user to the base station is thesampled data of the first parameter.

TABLE 1 Correspondence between bit values and transmitted data Bit valueTransmitted data 1 First parameter 0 Sampled data of the first parameter

In a possible implementation, the first indication information may bereplaced with a channel condition status. To be specific, the transmitend may choose, based on the channel condition status fed back by thereceive end, to send the first parameter or the sampled data of thefirst parameter. When the transmit end receives a good channel conditionfed back by the receive end, the transmit end sends the first parameter.When the transmit end receives a poor feedback channel condition, thetransmit end sends the sampled data of the first parameter.

FIG. 3 is a schematic flowchart of a data transmission method 300according to another embodiment of this application.

In S310, a transmit end determines a first dataset, where the firstdataset includes at least one piece of to-be-sent data.

In S320, the transmit end determines a first parameter based on thefirst dataset, where the first parameter includes a characteristicparameter of a function, and the function is obtained by modeling thedata in the first dataset.

Specifically, the function may be a Gaussian mixture model, and thefirst parameter includes at least one of a mean vector, a covariancematrix, or a mixing coefficient of the Gaussian mixture model.

It should be understood that, when the transmit end models the data inthe first dataset by using the Gaussian mixture model, it is firstassumed that a distribution of the data in the first dataset is a mixeddistribution formed by combining a plurality of multivariate Gaussiandistributions, and parameters (mean values and variances) of thesemultivariate Gaussian distributions are unknown, and may be representedby using the following formula:

$p(d) = {\sum\limits_{i = 1}^{N}{\alpha_{i} \cdot p\left( {d\left| {\mu_{i},\text{Σ}_{i}} \right)} \right)}}$

d is a sample in the dataset, ^(p(d)) represents a probabilitydistribution of the sample,

(μ_(i), Σ_(i))

is a mean vector and a covariance matrix of an i^(th) multivariateGaussian N distribution, α_(i) is a mixing coefficient, and satisfies

$0 \leq \alpha_{i} \leq 1,\mspace{6mu}\text{and}\mspace{6mu}{\sum\limits_{i = 1}^{N}{\alpha_{i} = 1}}.$

After the modeling is completed, the transmit end may identify andoptimize the parameters in the Gaussian mixture model by using aparameter estimation method, for example, an EM algorithm. Anexpectation value is maximized by updating two parameters, the meanvalue and the variance. This process can be iterative until parametersgenerated in two iterations change very little.

In a possible implementation, the function may alternatively be agenerative adversarial network (GAN), and the first parameter includesat least one of a weight or a bias of the GAN.

As an unsupervised learning method, the GAN is composed of two sets ofneural networks: a generator (G) and a discriminator (D). A trainingprocess of the GAN is roughly as follows: The generator G is configuredto generate a false sample, and the discriminator D is configured todistinguish whether the sample generated by the generator G is real dataor false data. A result of each determination is input to the G and theD as a back propagation input. If the D is correctly determined, aparameter of the G needs to be adjusted to make the generated false datamore realistic. If the D is incorrectly determined, a parameter of the Dneeds to be adjusted to avoid a next determining error. Trainingcontinues until the two parties enter a state of equilibrium andharmony. An ultimate goal of the GAN model is to obtain a generator withhigh quality and a discriminator with a strong determining ability.After the training is completed, parameters of the two neural networks,the G and the D, are determined, and the generator G may be used toreconstruct original data.

It should be understood that, when modeling is performed on the data inthe first dataset by using the GAN, the first parameter is obtained bytraining the data in the dataset by using the GAN model.

In S330, the transmit end sends the first parameter, andcorrespondingly, a receive end receives the first parameter.

In S340, the receive end determines the function based on the firstparameter.

In a possible implementation, after the receive end receives the firstparameter, the receive end determines the Gaussian mixture model basedon the mean value and the variance in the first parameter.

In a possible implementation, after receiving the first parameter, thereceive end determines the GAN based on the weight and the bias in thefirst parameter.

In S350, the receive end samples the function for M times, toreconstruct the data in the first dataset, where M is a positiveinteger.

In a possible implementation, the receive end samples the Gaussianmixture model for M times to obtain M pieces of sampled data, and thereceive end uses the M pieces of sampled data as reconstructed data inthe first dataset.

In a possible implementation, when the receive end samples the GANnetwork to obtain M pieces of sampled data, the receive end uses the Mpieces of sampled data as the reconstructed data in the first dataset.

Therefore, in this embodiment of this application, a first parameter istransmitted, so that a receive end can determine a function based on thefirst parameter, sample the function for a plurality of times, and usedata sampled for the plurality of times as reconstructed data in a firstdataset. Therefore, original data does not need to be transmitted, andoverheads can be reduced.

Preferably, in S350, M is greater than 1.

As a possible implementation, in S330, the first parameter furtherincludes a quantity N of pieces of data in the first dataset, where Mmay be a multiple of N, or M may be less than N, and N is a positiveinteger.

In this case, a transmit end may send a quantity of pieces of originaldata, and a quantity of pieces of data generated by a receive end is amultiple of the quantity of pieces of original data. In this way, thedata can be better used for training.

In a possible implementation, the method 300 further includes: Thereceive end sends first indication information to the transmit end,where the first indication information indicates the transmit end tosend the first parameter; and correspondingly, the transmit end receivesthe first indication information, and sends the first parameter based onthe first indication information.

In a possible implementation, before S310, that is, before the transmitend determines the first dataset, the method 300 further includes: thetransmit end determines a second dataset, where the second datasetincludes at least one piece of to-be-sent data; the transmit endclusters the data in the second dataset to obtain S data subsets, whereS is a positive integer; and the transmit end separately determines theS data subsets as the first dataset.

As an example instead of a limitation, a clustering method may beK-means clustering, mean shift clustering, a density-based clusteringmethod, or the like.

In other words, the transmit end may cluster the to-be-sent data in thesecond dataset based on a status of the data in the second dataset, andthen separately determine the clustered data as the first dataset. Inthis way, accuracy of sampling by the receive end can be furtherimproved while transmission resources are saved.

In a possible implementation, the method 300 further includes: Thetransmit end sends at least one piece of first data; andcorrespondingly, the receive end receives the at least one piece offirst data.

In this embodiment of this application, the data received by the receiveend further includes some data in the first dataset. The receive enduses the data and the data obtained by the receive end through samplingas a real sample for neural network training. In this way, authenticityof a training result can be improved.

In a possible implementation, S320 in the method 300 may alternativelybe implemented at the receive end. Specifically, the transmit endselects a small amount of data in the first dataset as a third dataset,and the transmit end sends the data in the third dataset.Correspondingly, the receive end receives the data in the third dataset,the receive end determines the first parameter based on the thirddataset, and the receive end stores the first parameter. When a largeamount of data needs to be used, the receive end generates the largeamount of data based on the first parameter. For a specific method fordetermining the first parameter and a method for generating data byusing the first parameter, refer to S320, S340, and S350 in the method300.

In this embodiment of this application, the transmit end mayalternatively send only a small amount of first data, and the receiveend receives the small amount of first data, determines a firstparameter based on the data at the receive end, and then generates alarge amount of data based on the data. In this way, space occupied fordata transmission and storage can be reduced. Therefore, a method fordetermining the first parameter and a method for using the firstparameter in this application may also be used by the receive end at thesame time. This is not limited in this application.

According to the foregoing method, FIG. 4 is a schematic diagram of adata transmission apparatus 400 according to an embodiment of thisapplication.

The apparatus 400 may be a terminal device, or may be a network device,or may be a chip or a circuit, for example, a chip or a circuit that maybe disposed in a terminal device.

The apparatus 400 may include a processing unit 410 (namely, an exampleof a processing unit). In a possible implementation, the apparatus 400may further include a storage unit 420. The storage unit 420 isconfigured to store instructions.

In a possible manner, the processing unit 410 is configured to executethe instructions stored in the storage unit 420, so that the apparatus400 implements the steps performed by the receive end in the foregoingmethod.

Further, the apparatus 400 may further include an input port 430(namely, an example of a communication unit) and an output port 440(namely, another example of the communication unit). Further, theprocessing unit 410, the storage unit 420, the input port 430, and theoutput port 440 may communicate with each other through an internalconnection path, to transfer a control signal and/or a data signal. Thestorage unit 420 is configured to store a computer program. Theprocessing unit 410 may be configured to invoke the computer programfrom the storage unit 420 and run the computer program to complete thesteps of the receive end in the foregoing method. The storage unit 420may be integrated into the processing unit 410, or may be disposedseparately from the processing unit 410.

In a possible manner, the input port 430 may be a receiver, and theoutput port 440 is a transmitter. The receiver and the transmitter maybe a same physical entity or different physical entities. When thereceiver and the transmitter are a same physical entity, the receiverand the transmitter may be collectively referred to as a transceiver.

In a possible manner, the input port 430 is an input interface, and theoutput port 440 is an output interface.

As an implementation, it may be considered that functions of the inputport 430 and the output port 440 are implemented by using a transceivercircuit or a dedicated transceiver chip. It may be considered that theprocessing unit 410 is implemented by using a dedicated processing chip,a processing circuit, a processing unit, or a general-purpose chip.

As another implementation, it may be considered that the communicationdevice (e.g., the receive end) provided in embodiments of thisapplication may be implemented by using a general-purpose computer. Tobe specific, program code for implementing functions of the processingunit 410, the input port 430, and the output port 440 is stored in thestorage unit 420, and a general-purpose processing unit executes thecode in the storage unit 420 to implement the functions of theprocessing unit 410, the input port 430, and the output port 440.

In an implementation, the input port 430 is configured to receive afirst parameter, where the first parameter includes a parameter of aprobability distribution of a hidden variable, and the hidden variableis obtained by encoding first data by using an encoder of a variationalauto-encoder.

The processing unit 410 is configured to: determine the probabilitydistribution based on the first parameter; sample the probabilitydistribution for M times, to obtain M pieces of sampled data, where M isa positive integer; and reconstruct the first data based on the M piecesof sampled data.

In a possible implementation, the output port 440 is configured to sendfirst indication information, where the first indication informationindicates the transmit end to send the first parameter.

In a possible implementation, the input port 430 is further configuredto receive second indication information, where the second indicationinformation indicates the receive end to sample the probabilitydistribution.

In a possible implementation, the storage unit 420 is configured tostore the first parameter.

Functions and actions of the modules or units in the apparatus 400listed above are merely examples for description. When the apparatus 400is configured in or is a receive end, the modules or units in theapparatus 400 may be configured to perform the actions or processingprocesses performed by the receive end in the foregoing method. Herein,to avoid repetition, detailed descriptions are omitted.

For concepts, explanations, detailed descriptions, and other steps ofthe apparatus 400 that are related to the technical solutions providedin embodiments of this application, refer to the descriptions of thecontent in the foregoing method or other embodiments. Details are notdescribed herein again.

According to the foregoing method, FIG. 5 is a schematic diagram of adata transmission apparatus 500 according to an embodiment of thisapplication.

The apparatus 500 may be a terminal device, or may be a network device,or may be a chip or a circuit, for example, a chip or a circuit that maybe disposed in an access device.

The apparatus 500 may include a processing unit 510 (namely, an exampleof a processing unit) and a storage unit 520. The storage unit 520 isconfigured to store instructions.

The processing unit 510 is configured to execute the instructions storedin the storage unit 520, so that the apparatus 500 implements the stepsperformed by the access device in the foregoing method.

Further, the apparatus 500 may further include an input port 530(namely, an example of a communication unit) and an output port 540(namely, another example of the communication unit). Further, theprocessing unit 510, the storage unit 520, the input port 530, and theoutput port 540 may communicate with each other through an internalconnection path, to transfer a control signal and/or a data signal. Thestorage unit 520 is configured to store a computer program. Theprocessing unit 510 may be configured to invoke the computer programfrom the storage unit 520 and run the computer program, to control theinput port 530 to receive a signal, and control the output port 540 tosend a signal, so as to complete the steps of the terminal device in theforegoing method. The storage unit 520 may be integrated into theprocessing unit 510, or may be disposed separately from the processingunit 510.

In a possible implementation, if the apparatus 500 is a communicationdevice (e.g., a transmit end), the input port 530 is a receiver, and theoutput port 540 is a transmitter. The receiver and the transmitter maybe a same physical entity or different physical entities. When thereceiver and the transmitter are a same physical entity, the receiverand the transmitter may be collectively referred to as a transceiver.

In a possible implementation, if the apparatus 500 is a chip or acircuit, the input port 530 is an input interface and the output port540 is an output interface.

As an implementation, it may be considered that functions of the inputport 530 and the output port 540 are implemented by using a transceivercircuit or a dedicated transceiver chip. It may be considered that theprocessing unit 510 is implemented by using a dedicated processing chip,a processing circuit, a processing unit, or a general-purpose chip.

As another implementation, it may be considered that the communicationdevice (e.g., the transmit end) provided in embodiments of thisapplication may be implemented by using a general-purpose computer. Tobe specific, program code for implementing functions of the processingunit 510, the input port 530, and the output port 540 is stored in thestorage unit 520, and a general-purpose processing unit executes thecode in the storage unit 520 to implement the functions of theprocessing unit 510, the input port 530, and the output port 540.

In an implementation, the processing unit 510 is configured to:determine a first dataset; and determine a first parameter based on thefirst data, where the first parameter includes a parameter of aprobability distribution of a hidden variable, and the hidden variableis obtained by encoding the first data by using an encoder of avariational auto-encoder.

The output port 540 is configured to send the first parameter.

In a possible implementation, the input port 530 is configured toreceive first indication information, where the first indicationinformation indicates the transmit end to send the first parameter.

In a possible implementation, the output port 540 is further configuredto send second indication information, where the second indicationinformation indicates the receive end to sample the probabilitydistribution.

In a possible implementation, the storage unit 520 is configured tostore the first parameter.

Functions and actions of the modules or units in the apparatus 500listed above are merely examples for description. When the apparatus 500is configured in or is the access device, the modules or units in theapparatus 500 may be configured to perform the actions or processingprocesses performed by the access device in the foregoing method.Herein, to avoid repetition, detailed descriptions thereof are omitted.

For concepts, explanations, detailed descriptions, and other steps ofthe apparatus 500 that are related to the technical solutions providedin embodiments of this application, refer to the descriptions of thecontent in the foregoing method or other embodiments. Details are notdescribed herein again.

According to the foregoing method, FIG. 6 is a schematic diagram of adata transmission apparatus 600 according to an embodiment of thisapplication.

The apparatus 600 may be a terminal device, or may be a network device,or may be a chip or a circuit, for example, a chip or a circuit that maybe disposed in a terminal device.

The apparatus 600 may include a processing unit 610 (namely, an exampleof a processing unit). In a possible implementation, the apparatus 600may further include a storage unit 620. The storage unit 620 isconfigured to store instructions.

In a possible manner, the processing unit 610 is configured to executethe instructions stored in the storage unit 620, so that the apparatus600 implements the steps performed by the receive end in the foregoingmethod.

Further, the apparatus 600 may further include an input port 630(namely, an example of a communication unit) and an output port 640(namely, another example of the communication unit). Further, theprocessing unit 610, the storage unit 620, the input port 630, and theoutput port 640 may communicate with each other through an internalconnection path, to transfer a control signal and/or a data signal. Thestorage unit 620 is configured to store a computer program. Theprocessing unit 610 may be configured to invoke the computer programfrom the storage unit 620 and run the computer program to complete thesteps of the receive end in the foregoing method. The storage unit 620may be integrated into the processing unit 610, or may be disposedseparately from the processing unit 610.

In a possible manner, the input port 630 may be a receiver, and theoutput port 640 is a transmitter. The receiver and the transmitter maybe a same physical entity or different physical entities. When thereceiver and the transmitter are a same physical entity, the receiverand the transmitter may be collectively referred to as a transceiver.

In a possible manner, the input port 630 is an input interface, and theoutput port 640 is an output interface.

As an implementation, it may be considered that functions of the inputport 630 and the output port 640 are implemented by using a transceivercircuit or a dedicated transceiver chip. It may be considered that theprocessing unit 610 is implemented by using a dedicated processing chip,a processing circuit, a processing unit, or a general-purpose chip.

As another implementation, it may be considered that the communicationdevice (e.g., the receive end) provided in embodiments of thisapplication may be implemented by using a general-purpose computer. Tobe specific, program code for implementing functions of the processingunit 610, the input port 630, and the output port 640 is stored in thestorage unit 620, and a general-purpose processing unit executes thecode in the storage unit 620 to implement the functions of theprocessing unit 610, the input port 630, and the output port 640.

In an implementation, the input port 630 is configured to receive afirst parameter, where the first parameter includes a characteristicparameter of a function, the function is obtained by modeling data inthe first dataset, and the first dataset includes at least one piece ofto-be-sent data.

The processing unit 610 is configured to: determine the function basedon the first parameter; and sample the function for M times, toreconstruct the data in the first dataset, where M is a positiveinteger.

In a possible implementation, the input port 630 is configured toreceive first indication information, where the first indicationinformation indicates the transmit end to send the first parameter.

In a possible implementation, the storage unit 620 is configured tostore the first parameter.

Functions and actions of the modules or units in the apparatus 600listed above are merely examples for description. When the apparatus 600is configured in or is a receive end, the modules or units in theapparatus 600 may be configured to perform the actions or processingprocesses performed by the receive end in the foregoing method. Herein,to avoid repetition, detailed descriptions are omitted.

For concepts, explanations, detailed descriptions, and other steps ofthe apparatus 600 that are related to the technical solutions providedin embodiments of this application, refer to the descriptions of thecontent in the foregoing method or other embodiments. Details are notdescribed herein again.

According to the foregoing method, FIG. 7 is a schematic diagram of adata transmission apparatus 700 according to an embodiment of thisapplication.

The apparatus 700 may be a terminal device, or may be a network device,or may be a chip or a circuit, for example, a chip or a circuit that maybe disposed in a terminal device.

The apparatus 700 may include a processing unit 710 (namely, an exampleof a processing unit). In a possible implementation, the apparatus 700may further include a storage unit 720. The storage unit 720 isconfigured to store instructions.

In a possible manner, the processing unit 710 is configured to executethe instructions stored in the storage unit 720, so that the apparatus700 implements the steps performed by the receive end in the foregoingmethod.

Further, the apparatus 700 may further include an input port 730(namely, an example of a communication unit) and an output port 740(namely, another example of the communication unit). Further, theprocessing unit 710, the storage unit 720, the input port 730, and theoutput port 740 may communicate with each other through an internalconnection path, to transfer a control signal and/or a data signal. Thestorage unit 720 is configured to store a computer program. Theprocessing unit 710 may be configured to invoke the computer programfrom the storage unit 720 and run the computer program to complete thesteps of the receive end in the foregoing method. The storage unit 720may be integrated into the processing unit 710, or may be disposedseparately from the processing unit 710.

In a possible manner, the input port 730 may be a receiver, and theoutput port 740 is a transmitter. The receiver and the transmitter maybe a same physical entity or different physical entities. When thereceiver and the transmitter are a same physical entity, the receiverand the transmitter may be collectively referred to as a transceiver.

In a possible manner, the input port 730 is an input interface, and theoutput port 740 is an output interface.

As an implementation, it may be considered that functions of the inputport 730 and the output port 740 are implemented by using a transceivercircuit or a dedicated transceiver chip. It may be considered that theprocessing unit 710 is implemented by using a dedicated processing chip,a processing circuit, a processing unit, or a general-purpose chip.

As another implementation, it may be considered that the communicationdevice (e.g., the transmit end) provided in embodiments of thisapplication may be implemented by using a general-purpose computer. Tobe specific, program code for implementing functions of the processingunit 710, the input port 730, and the output port 740 is stored in thestorage unit 720, and a general-purpose processing unit executes thecode in the storage unit 720 to implement the functions of theprocessing unit 710, the input port 730, and the output port 740.

In an implementation, the processing unit 710 is configured to:determine a first dataset, where the first dataset includes at least onepiece of to-be-sent data; and determine a first parameter based on thefirst dataset, where the first parameter includes a characteristicparameter of a function, and the function is obtained by modeling thedata in the first dataset.

The output port 740 is configured to send the first parameter.

In a possible implementation, the storage unit 720 is configured tostore the first parameter.

In a possible implementation, the input port 730 is configured toreceive first indication information, where the first indicationinformation indicates the transmit end to send the first parameter.

In a possible implementation, the processing unit 710 is furtherconfigured to: determine a second dataset, where the second datasetincludes at least one piece of to-be-sent data; cluster the data in thesecond dataset to obtain S data subsets, where S is a positive integer;and separately determine the S data subsets as the first dataset.

In a possible implementation, the storage unit 720 is configured tostore the first parameter.

Functions and actions of the modules or units in the apparatus 700listed above are merely examples for description. When the apparatus 700is configured in or is a receive end, the modules or units in theapparatus 700 may be configured to perform the actions or processingprocesses performed by the receive end in the foregoing method. Herein,to avoid repetition, detailed descriptions are omitted.

For concepts, explanations, detailed descriptions, and other steps ofthe apparatus 700 that are related to the technical solutions providedin embodiments of this application, refer to the descriptions of thecontent in the foregoing method or other embodiments. Details are notdescribed herein again.

According to the method in embodiments of this application, anembodiment of this application further provides a communication system,including the transmit end and the receive end described above.

It should be understood that, the processor in embodiments of thisapplication may be a central processing unit (CPU), or may be anothergeneral-purpose processor, a digital signal processor (digital signalprocessor, DSP), an application-specific integrated circuit (ASIC), afield programmable gate array (FPGA), or another programmable logicdevice, discrete gate or transistor logic device, discrete hardwarecomponent, or the like. The general-purpose processor may be amicroprocessor, or the processor may be any conventional processor orthe like.

It may be understood that the memory in embodiments of this applicationmay be a volatile memory or a nonvolatile memory, or may include avolatile memory and a nonvolatile memory. The nonvolatile memory may bea read-only memory (ROM), a programmable read-only memory (programmableROM, PROM), an erasable programmable read-only memory (erasable PROM,EPROM), an electrically erasable programmable read-only memory(electrically EPROM, EEPROM), or a flash memory. The volatile memory maybe a random access memory (RAM), used as an external cache. Through anexample rather than a limitative description, random access memories(random access memories, RAMs) in many forms may be used, for example, astatic random access memorystatic RAM, SRAM), a dynamic random accessmemory (DRAM), a synchronous dynamic random access memory (synchronousDRAM, SDRAM), a double data rate synchronous dynamic random accessmemory (double data rate SDRAM, DDR SDRAM), an enhanced synchronousdynamic random access memory (enhanced SDRAM, ESDRAM), a synchlinkdynamic random access memory (synchlink DRAM, SLDRAM), and a directrambus random access memory (direct rambus RAM, DR RAM).

All or some of the foregoing embodiments may be implemented usingsoftware, hardware, firmware, or any combination thereof. When thesoftware is used to implement embodiments, all or some of the foregoingembodiments may be implemented in a form of a computer program product.The computer program product includes one or more computer instructionsor computer programs. When the computer instructions or the computerprograms are loaded or executed on a computer, the procedures orfunctions according to embodiments of this application are all orpartially generated. The computer may be a general-purpose computer, adedicated computer, a computer network, or another programmableapparatus. The computer instructions may be stored in acomputer-readable storage medium or may be transmitted from onecomputer-readable storage medium to another computer-readable storagemedium. For example, the computer instructions may be transmitted from awebsite, computer, server, or data center to another website, computer,server, or data center in a wired (e.g., infrared, radio, or microwave)manner. The computer-readable storage medium may be any usable mediumaccessible by a computer, or a data storage device, such as a server ora data center, integrating one or more usable media. The usable mediummay be a magnetic medium (e.g., a floppy disk, a hard disk, or amagnetic tape), an optical medium (e.g., a DVD), or a semiconductormedium. The semiconductor medium may be a solid-state drive.

It should be understood that the term “and/or” in this specificationdescribes only an association relationship between associated objectsand indicates that three relationships may exist. For example, A and/orB may indicate the following three cases: Only A exists, both A and Bexist, and only B exists. In addition, the character “/” in thisspecification generally indicates an “or” relationship between theassociated objects.

It should be understood that sequence numbers of the foregoing processesdo not mean execution sequences in various embodiments of thisapplication. The execution sequences of the processes should bedetermined based on functions and internal logic of the processes, andshould not be construed as any limitation on the implementationprocesses of embodiments of this application.

A person of ordinary skill in the art may be aware that, in combinationwith the examples described in embodiments disclosed in thisspecification, units and algorithm steps may be implemented byelectronic hardware or a combination of computer software and electronichardware. Whether the functions are performed by hardware or softwaredepends on particular applications and design constraint conditions ofthe technical solutions. A person skilled in the art may use differentmethods to implement the described functions for each particularapplication, but it should not be considered that the implementationgoes beyond the scope of this application. It may be clearly understoodby a person skilled in the art that, for the purpose of convenient andbrief description, for a detailed working process of the foregoingsystem, apparatus, and unit, refer to a corresponding process in theforegoing method embodiments. Details are not described herein again. Inthe several embodiments provided in this application, it should beunderstood that the disclosed system, apparatus, and method may beimplemented in other manners. For example, the described apparatusembodiment is merely an example. For example, division into the units ismerely logical function division and may be other division in actualimplementation. For example, a plurality of units or components may becombined or integrated into another system, or some features may beignored or not performed. In addition, the displayed or discussed mutualcouplings or direct couplings or communication connections may beimplemented through some interfaces. The indirect couplings orcommunication connections between the apparatuses or units may beimplemented in electronic, mechanical, or other forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected based on actualrequirements to achieve the objectives of the solutions of embodiments.In addition, function units in embodiments of this application may beintegrated into one processing unit, each of the units may exist alonephysically, or two or more units are integrated into one unit. When thefunctions are implemented in a form of a software function unit and soldor used as an independent product, the functions may be stored in acomputer-readable storage medium. Based on such an understanding, thetechnical solutions of this application essentially, or the partcontributing to the conventional technology, or some of the technicalsolutions may be implemented in a form of a software product. Thecomputer software product is stored in a storage medium, and includesseveral instructions to enable a computer device (which may be apersonal computer, a server, a network device, or the like) to performall or some of the steps of the methods described in embodiments of thisapplication. The foregoing storage medium includes any medium that canstore program code, such as a USB flash drive, a removable hard disk, aread-only memory, a random access memory, a magnetic disk, or an opticaldisc.

The foregoing descriptions are merely specific implementations of thisapplication, but are not intended to limit the protection scope of thisapplication. Any variation or replacement readily figured out by aperson skilled in the art within the technical scope disclosed in thisapplication shall fall within the protection scope of this application.Therefore, the protection scope of this application shall be subject tothe protection scope of the claims.

1. A data transmission method carried out by a receive end, the methodcomprising: receiving, a first parameter, wherein the first parametercomprises a parameter of a probability distribution of a hiddenvariable, and the hidden variable is obtained by encoding first data byusing an encoder of a variational auto-encoder; determining, theprobability distribution based on the first parameter; sampling, theprobability distribution for M times, to obtain M pieces of sampleddata, wherein M is a positive integer; and reconstructing, the firstdata based on the M pieces of sampled data.
 2. The method according toclaim 1, wherein the reconstructing the first data based on the M piecesof sampled data comprises: separately decoding, the M pieces of sampleddata by using a decoder of the variational auto-encoder, to obtain Mpieces of second data; and determining, reconstructed first data basedon the M pieces of second data, wherein the determining, reconstructedfirst data based on the M pieces of second data comprises performing atleast one operation from the group consisting of: averaging, the Mpieces of second data, and using an averaging result as thereconstructed first data; performing, selection on the M pieces ofsecond data, and using a selection result as the reconstructed firstdata; and averaging, the M pieces of second data to obtain third data,performing, a decision on the third data, and using a decision result asthe reconstructed first data.
 3. The method according to claim 1,wherein before the receiving, a first parameter, the method furthercomprises: sending, first indication information, that indicates to atransmit end to send the first parameter.
 4. The method according toclaim 1, wherein the method further comprises: receiving, secondindication information, that indicates to the receive end to sample theprobability distribution, and wherein the sampling, the probabilitydistribution for M times comprises sampling, the probabilitydistribution for the M times based on the second indication information.5. A data transmission method carried out by a transmit end, the methodcomprising: determining, first data; determining, a first parameterbased on the first data, wherein the first parameter comprises aparameter of a probability distribution of a hidden variable, and thehidden variable is obtained by encoding the first data by using anencoder of a variational auto-encoder; and sending, the first parameter.6. The method according to claim 5, wherein the method furthercomprises: receiving, first indication information, that indicates tothe transmit end to send the first parameter, wherein the sending, bythe transmit end, the first parameter comprises: sending, by thetransmit end, the first parameter based on the first indicationinformation.
 7. The method according to claim 5, wherein the methodfurther comprises: sending, second indication information, thatindicates to a receive end to sample the probability distribution. 8-13.(canceled)
 14. A data transmission method carried our by a transmit end,the method comprising: determining, a first dataset, wherein the firstdataset comprises at least one piece of to-be-sent data; determining, afirst parameter based on the first dataset, wherein the first parametercomprises a characteristic parameter of a function, and the function isobtained by modeling the data in the first dataset; and sending, thefirst parameter.
 15. The method according to claim 14, wherein thefunction comprises a Gaussian mixture model, and wherein the firstparameter comprises at least one of the group consisting of: a meanvector, a covariance matrix, and a mixing coefficient of the Gaussianmixture model.
 16. The method according to claim 14, wherein thefunction comprises a generative adversarial network (GAN), and whereinthe first parameter comprises at least one of a weight or a bias of theGAN.
 17. The method according to claim 14, wherein the first parameterfurther comprises a quantity N of pieces of data in the first dataset,and wherein N is a positive integer.
 18. The method according to claim14, wherein the method further comprises: receiving, first indicationinformation, that indicates the transmit end to send the firstparameter, and wherein the sending, the first parameter comprisessending the first parameter based on the first indication information.19. The method according to claim 14, wherein the method furthercomprises: sending, second indication information, and wherein thesecond indication information indicates a receive end to sample aprobability distribution.
 20. The method according to claim 14, whereinbefore the determining, a first dataset, the method further comprises:determining, a second dataset, wherein the second dataset comprises atleast one piece of to-be-sent data; clustering, the data in the seconddataset to obtain S data subsets, wherein S is a positive integer; andseparately determining, the S data subsets as the first dataset. 21-44.(canceled)